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Archives for June 2016

Three Dimensions of Storage Sizing & Design – Part 2: Workloads

June 23, 2016 By Nexenta

By Edwin Weijdema, Sales Engineer, Nexenta

In this second post of the multi-post Three Dimensions of Storage Sizing & Design we will dive deeper in the dimension: Use and specifically the application Workloads part. By developing an understanding of the different kind of workloads and their characteristics will enable you to look at your different workloads and determine what impact that will have on the design and sizing. Knowing, understanding and classifying which applications will run as a workload interacting with the storage, gives you an important puzzle piece.

Knowing workloads

Applications have been created to support us and automate processes to work efficient and swift with the available data. Today we use, protect and manage an overwhelming amount of data that is being transformed into information through all kinds of applications. Most of these applications also interact with our storage systems. Overlooking the divers application landscape and how they interact with the storage systems(s), we can organize them in several categories where the most common ones are:

Transactional Enterprise Applications – are used to work with data that triggers an internal or external event or transaction that takes place as an organization conducts its business. A lot of people also like to simplify this by calling it the database backend. For example an online ordering system, where orders are being entered through a web interface, and than stored in a database.  (e.g. SQL, Exchange, CRM, e-Business, Oracle, SAP, PostgreSQL, Sharepoint, Call Centre Systems, etc.)

Virtualization – translates the physical hardware and operating system the application runs on and creates a virtual machine in the form of a few files gathered in a folder. Most organizations are using Virtual Machines and/or Virtual Desktops to run their applications. This is a great way to consolidate infrastructures onto a few physical machines, reducing costs and making infrastructures more agile and flexible. By consolidating the different applications, servers and desktops onto a virtual environment also consolidates and changes the I/O data path to and from the storage. (e.g. VMware vSphere, VMware Horizon, Microsoft Hyper-V, Citrix Xenserver, Cloudstack, Openstack, etc.)

Generic Enterprise Fileservers – stores small to large files from all kinds of applications. Often you will see documents, pictures, media files and such. Any sort of file saved by applications and usually accessed over SMB/CIFS/NFS protocol.

Back-up & Archive – back-up and archiving systems have two distinct and complementary functions within an enterprise: backup for high-speed copy and restore to minimize the impact of failures, human error or disaster; and archiving to effectively manage data for retention and long-term access and retrieval. (e.g. Veeam, Commvault, Veritas, etc.)

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Understanding workloads

Understanding workloads and specifically the Disk I/O pattern of enterprise applications helps tremendous with designing and sizing your storage solution(s). So you can make sure that your users, with their applications, are being optimal supported by the storage system. Vendors of enterprise application software often do not inform users about their workload characteristics. This is because the same application may generate different workload patterns depending upon the user’s configuration, or they simply do not know!

Workload characteristics

In this post we will just do a high level overview of the different workload characteristics and the relationship between them. In the next post we will dive deeper into the technical background of speed, throughput and response.

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Workloads have several characteristics that define the workload type:

  • Speed – measured in IOPS (Input/Output Per Second), defines the IOs per second. Read and/or Write IOs can be of different patterns (for example, sequential and random). The higher the IOPS the better the performance.
  • Throughput – measured in MB/s, defines data transfer rate also often called bandwidth. The higher the throughput, the more data that can be processed per second.
  • Response – measured in time like ns/us/ms latency, defines the amount of time the IO needs to complete. The lower the latency the faster a system/application/data responds, the more fluid it looks to the user. There are many latency measurements that can be taken throughout the whole data path.

 Random versus Sequential access pattern

While looking at your applications and how they access their data gives you a good indication if the access pattern is random or sequential. Sequential access means all data blocks will be accessed/written after each other so 1 > 2 > 3 > 4 > 5 where Random access can mean 5 > 1 > 3 > 2 > 4. Accessing data sequentially is much faster than accessing it randomly, because of the way disk hardware works. With spinning disk the seek operation takes the most time in the whole I/O process. The disk head needs to position itself at the correct disk platter to access the requested data. Randomly reading data takes a larger number of seek operations than sequential reading, meaning that the throughput with random will be much lower than sequential. The same applies to random writing. Examining the used workloads might be useful for designing and sizing the storage system. You could use for instance Lakeside Systrack software to give a good indication in the way the workload runs and interacts with the storage.

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I/O Size in the mix

The amount of throughput we can achieve is dependent on the pattern to be random or sequential and the I/O size that is being issued. For a workload with an I/O size of 4k block-size you can calculate the throughput by multiplying the number of IOPS times the I/O size.

Throughput in MB/sec = (IOPS x I/O size) / 1024

So 10.000 IOPS with an 4k block-size will be (10.000 x 4k)/1024 = 39.06 MB/sec throughput you can achieve. The whole data path should be addressed too get a clear insight and more accurate number though!

Looking at a random workload you will see that latency will start to kick in which will reduce the number of IOPS that can be achieved by the storage so random IOPS can reduce the amount of throughput significantly. A SSD produces much more IOPS and throughput against very low latency, because it does not contain moving parts. So why not go all flash than? Its all about knowing and understanding your workloads and deciphering if you need that amount of power against the cost associated with it. Balancing costs against requirements is the best way to go forward.

Classifying workloads

Lets start classifying the different workloads we identified, with the different characteristics that are important for storage. Looking at the four most common workloads:

Transactional Enterprise Applications

Transactional Enterprise Applications often are backed by a database where response is key. Looking at an Oracle database you will see that by decreasing latency will reduce the Wait states and by decreasing the Wait states the usage of CPU capacity will be reduced. If you are running virtualized Oracle database you might want to consider reducing latency and free up CPU cycles so you can run more database on that same CPU, therefore reducing your Oracle licensing cost.

Transactional Enterprise Applications tend to have a high amount of small random read I/O and a desire for fast response (low latency).

Virtualization

Virtualization is a unique workload because it consolidates several different application workloads with their corresponding characteristics on the hypervisor. So you will have several applications doing for instance random 4k, 8k and 16k blocks within the VM but the hypervisor absorbs those in memory and transforms them into 1MB or 2MB blocks which it sends and requests from the storage. VMware ESXi with VMFS5 uses 1MB unified blocks, while Microsoft Hyper-V uses 2MB blocks.

Virtualization and its consolidation also bring some storage benefit because not every application workload is going to peak at the same time. So rather than over provision it is better to look at the total picture.

Virtualization workloads tend to be fully random with a desire for speed (IOPS).

 Generic Enterprise Fileserver

Generally a File Share is used by lots of users in an organization and different applications storing their data on those file shares. Users will open a file and work in/with it for some time before saving it to the file share again. So throughput to get and save the file for the user is often most important. But depending on the applications storing files it can also be that speed is required in terms of IOPS.

Generic Enterprise Fileserver workloads tend to be often sequential, but can be random with a desire for throughput (MB/s). 

 Back-up & Archive

Back-up & Archive workloads look like a workload that would benefit the most by maximizing throughput. This is really depending on how you use the application and which options you configure.

Lets look at for instance Veeam, the performance of many Veeam Backup & Replication functions, such as Reverse Incremental, Synthetic Full, SureBackup and Instant Restore are most impact by the ability of the storage array to deliver random IOPS.

Veeam Backup & Replication offers two primary modes for storing backups to disk, Forward and Reverse incremental. Due to the differences in how these modes write backups to disk they have very different storage I/O profiles.

Forward Incremental backups offer the advantage that they perform only sequential writes to the target repository meaning that performance is significantly higher than reverse incremental backups. However, this backup mode does come with costs, primarily in the requirement to schedule periodic full backups. These backups will take additional time to create and, based on the method, addition space on the target repository.

There are three options for creating new full backups:

Synthetic Full — this method uses the most recent full backup, and any incremental backups created since then and builds a new full backup using this data. This requires space for a new full backup, and random I/O on the target repository and can take a long time to process.

Synthetic Full w/Transform — this method uses the most recent full backup, and any incremental backups created since then and builds a new full backup using this data, while converting the incremental backups to reverse incremental files. This requires only a small amount of additional space on the target repository, but usage a large amount of random I/O and can take a very long time to process

Active Full — this method simply runs a new full backup by reading all data from the source VMs. This requires I/O on the source storage, enough space to store a new full backup and sequential write I/O on the target repository.

Back-up & Archive workloads tend to be often sequential, but can be fully random with a high desire for throughput (MB/s) and speed (IOPS).

Summary

Interestingly, workload characterization is not only useful for sizing and designing storage systems but also useful for application developers to help them optimize their I/O routines or to document best practices based on such analysis. In the next part of Three Dimensions of Storage Sizing & Design we will dive deeper into workload characteristics speed (IOPS), throughput (MB/s) and Response (ms).

 

Raise Your SDS IQ (5 of 6): Practical Review of Virtual Storage Appliances

June 21, 2016 By Nexenta

By Michael Letschin, Field CTO

This is the fifth of six posts (the last one was Hyperconverged “SDS”) where we’re going to cover some practical details that help raise your SDS IQ and enable you to select the SDS solution that will deliver Storage on Your Terms. The fifth SDS flavor in our series is Virtual Storage Appliances.

One of the lesser known flavors of SDS is the Virtual Storage Appliance (VSA); it’s less common because it requires a virtual machine environment (think Microsoft’s Hyper-V or VMware), and because there are only a few, software-only options for it (like FreeNAS, LeftHand VSA, or Nexenta’s NexentaStor).   There also tend to be feature limitations as a result of the virtual machine environment, such as lack of replication across hosts. That said, for the right use case – such as remote offices, branch offices, small and medium-sized businesses, and multitenant apps – a virtual storage appliance (VSA) can offer a cost-effective storage solution. To use the VMware example, multiple head nodes each connect with vSphere and a VSA to present storage as one or more pools, with data from one or more VSAs.

Virtual storage appliances offer excellent flexibility through the hypervisor, because you can choose which one you’d like. The hypervisor host does confine scalability to VM size, that’s an important limitation. While managing individual VSAs across different hosts can bring some challenges, it also supports flexibility. You can spin up VSAs per group or per application to create multitenancy using VSAs. IT maintains control, even though individual teams might think they’re managing their storage pools. This approach offers good performance: even a loaded hypervisor only has minimal impact. And finally, this model doesn’t require any additional hardware, so there’s a nice cost benefit.

Overall grade: C

See below for a typical build and the report card:

Screen Shot 2016-06-21 at 1.58.47 PM

Raise Your SDS IQ (4 of 6): Practical Review of Hyperconverged “SDS”

June 14, 2016 By Nexenta

by Michael Letschin, Field CTO

This is the fourth of six posts (the last one was Scale-out) where we’re going to cover some practical details that help raise your SDS IQ and enable you to select the SDS solution that will deliver Storage on Your Terms. The fourth SDS flavor in our series is Hyperconverged “SDS”.

Hyperconverged systems are the subject of much industry hype and analyst debate. Some consider hyperconverged systems to be a form of SDS, others keep them out of the category for not having software-only options. What to know: a hyperconverged system is a single integrated hardware and software system comprising multiple head nodes that present all storage as one virtual pool (think Nutanix or VMware’s EVO Rail). This means that some of the software-only SDS benefits – like flexibility and cost effectiveness – are severely limited.

That said, because it’s fast and easy to set up and drop in a hyperconverged system, it’s a good choice for branch offices or green-field deployments, where there are no existing storage systems to integrate with. Hyperconverged systems are somewhat of a “black box”– meaning you’re not going to have access to software to tune – but you can dial up the performance by increasing the number of nodes.

The downside of Hyperconverged “SDS” is that it’s difficult to impossible to change the system later. Hyperconverged “SDS” provides building block only. You buy what the vendor is selling, which narrows your options. Because you’re tied to a vendor and their pricing models, cost efficiency is also limited. Plus, you’ll need to buy equal amounts of storage and compute capacity. Unless you’re an organization where requirements for storage and compute capacity scale in perfect step, this means you’ll end up with too much of one or the other, wasting part of your investment.

Overall grade: C

See below for a typical build and the report card:

Screen Shot 2016-06-14 at 10.19.33 AM

Raise Your SDS IQ (3 of 6): Practical Review of Scale-out

June 7, 2016 By Nexenta

This is the third of six posts (the last one was Scale-up Software-Only “SDS”) where we’re going to cover some practical details that help raise your SDS IQ and enable you to select the SDS solution that will deliver Storage on Your Terms. The third SDS flavor in our series is Scale-out.

Scale-out is a fundamentally different approach from scale-up; with Scale-out, multiple head nodes can be attached over the network to dramatically increase scalability. This is a broad category, and solutions for it could be either vendor-defined / hardware based (think EMC’s Isilon), or software-only (Nexenta’s NexentaEdge); while we’d consider the software-only approach to be the SDS version, the technical benefits of either type of scale out are similar. You use low-latency networking to connect as many nodes as you want and form a cluster that provides storage services out to applications as unified name space.

The scale-out approach works well for Archive or Web 2.0 applications use cases. Scalability is top notch, because you can start small and grow just by adding nodes. While it provides the performance needed to handle huge capacities, there’s an important dependence on the network – the quality of your gear will significantly impact performance, because of the amount of communication between nodes; that may mean that your IOPS aren’t great

The flexibility of scale-out SDS is generally good but currently offers limited protocol support. Often the maturity of the platforms themselves limits your flexibility; for example, you can’t use Exchange to write to an object back end. Likewise, object-oriented applications won’t work with some back ends, either. Protocol support considerations also impact the cost effectiveness of Scale-out: they may restrict your hardware choices and lock you in to more expensive purchases.

Overall grade: B

See below for a typical build and the report card:

Screen Shot 2016-06-07 at 11.26.20 AM

Lenovo Selects Nexenta for Lenovo StorSelect Program; Introduces Joint OpenSDS-Based Enterprise Storage Solution

June 6, 2016 By Nexenta

Earlier today, you may have heard about Lenovo’s StorSelect Program, which allows customers to confidently deploy new storage technologies using pre-loaded appliances with software from best-of-breed, Independent Software Vendors (ISVs) on proven Lenovo systems. The StorSelect solutions enable easy expansion for scalable deployments, as well as integration with existing storage infrastructures. The program will also offer quality, engineering expertise and access to Lenovo’s worldwide global services.

In March 2016, we announced our partnership with Lenovo as part of this program with a shared initiative to drive broader adoption of next-generation software storage solutions. We introduced a joint solution that integrates our award-winning, software-defined storage (SDS) with Lenovo x86 servers: The Lenovo Storage DX8200N powered by NexentaStor.

The Lenovo Storage DX8200N powered by NexentaStor allows you to accelerate and simplify unified file and block storage deployments. At the same time, this pre-validated turnkey solution provides easy scalability and simplified management at a fraction of the cost of legacy systems—without trade-offs in availability, reliability or functionality.

The integrated solutions will offer Lenovo customers worldwide, both scale-up and scale-out reference architectures on all flash, hybrid and spinning media systems. These solutions with Lenovo’s industry leading service and support will deliver a new SDS standard.

Target use cases for the Lenovo Storage DX8200N powered by NexentaStor solution include:

  • Unified File (NFS and SMB) and Block (Fibre Channel and iSCSI) services
  • VMware cloud backend storage
  • OpenStack and CloudStack backend storage
  • Generic NAS file services and Home Directory storage
  • Near-line archive and large scale backup repositories

“Lenovo recognizes Software-Defined Storage as the strategic storage solution to deliver revolutionary economics for enterprise and cloud storage needs,” said David Lincoln, General Manager of the Storage Business Unit at Lenovo. “We have selected Nexenta as a key partner for our StorSelect Program and are leveraging Nexenta’s deep storage software R&D skills and Lenovo’s proven enterprise servers to deliver an innovative, trusted, and scalable enterprise storage solution. Lenovo Storage DX8200N powered by NexentaStor will give customers the freedom to scale their data to meet business needs today and tomorrow.”

While pricing will vary on capacity and media types, you can expect affordable cents per gigabyte pricing for complete systems including three years of technical support.

Additional Resources:

Lenovo Storage DX8200N powered by NexentaStor data sheet

Lenovo StorSelect video

Lenovo StorSelect 3D tour

Lenovo StorSelect analyst paper

Lenovo and Nexenta: Strategic Partnership to Redefine Open Software-Defined Storage Solutions webinar

Lenovo StorSelect blog

If you’re planning to attend Lenovo’s Tech World conference on June 9, 2016 in San Francisco, please visit the Data Center Group showcase at the Fairmont to see demos of the new Lenovo Storage DX8200N powered by NexentaStor solution.

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